performance comparison of wavelet family based … · performance comparison of wavelet family...

Performance Comparison of wavelet family based Bio-Medical Image compression

Mr. Shubham Yadav1, Mrs. Shikha Singh2, Mrs. Akanksha Awasthi3 1M.Tech. Scholar, ECE Department, CVRU, Bilaspur

2Asst.Professor, ECE Department, CVRU, Bilaspur 3Asst.Professor, ECE Department, CVRU, Bilaspur

[email protected], [email protected], [email protected] Abstract- In paper, An attempt has been made to examine and

analyze the compression capability of the different wavelet

family on the bio-medical images. Different wavelet

function are used to produce the compressed bio-medical

images while keeping the PSNR(Peak signal to noise ratio )

constant. A simulation program has been developed under

MATLAB platform to study the above mentioned cause.

Conclusion is drawn on the basis of experimentation

through the simulation program.

Keywords- DCT(Discrete Cosine Transform),DWT(Discrete Wavelet

Transform), JPEG(Joint Picture Expert Group)MRI(Magnetic

Resonance Imaging)

I. INTRODUCTION

The process of enc

The process of encoding or representing the

information or data in lesser bits is called

compression. Compression is advantageous

because it helps us to reduce the storage

requirement of the information or data in storage

devices like hard disk, CD ROM etc.

[1],[2]Compression also help us to send the data

or information through internet by occupying the

lesser bandwidth. With compression, we can use

the storage devices or internet facility in a very

economical ways. On the other hand compression

has some drawbacks or limitation[3],[4].

Compression in the information or data is

achieved against the quality of the information.

Compression rendered the information loss in

quality. One more disadvantage is that it require

decompression scheme for decompressing the

data which require extra hardware and hence

become costly. For example, for compressing the

digital image require an extra hardware to

decompress the data. From this discussion it is

clear that a good compression scheme must be

trade off between the amount of compression,

amount of distortion produced due to the

compression and also the computational time and

resources[5-7].

Image compression is one of the way of data

compression in which the main goal is to reduce

the number bytes using to represent the graphics

image by keeping the distortion in image to the

acceptable level. The reduction in bytes of the

image means reduction in the size of the image

and hence more images can be accommodated in

a given storage devices. Compression also

reduce the bandwidth requirement of the internet

Shubham Yadav et al, International Journal of Computer Science & Communication Networks,Vol 5(5),338-347

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and also the time required to send the information

across the world. Compression in which some

information is lost is known as the lossy

compression while the one in which no

information is lost is known as the lossless

compression. Transform domain coding and

predictive coding comes under the lossy

compression. Transform domain methods are

useful for compression as well as for hiding

secret data[8]

There are many algorithms for compressing the

images. In internet, two most widely used image

compression scheme are JPEG (Joint Picture

Expert Group) and GIF. JPEG scheme is used for

photograph while the GIF scheme of

compression is used for the images where the

geometrical shapes are simple.

Medical imaging as describe earlier require

compression but without the loss of information .

most of the medical images like x-ray images,

MRI images ultrasound images and mammogram

images contain lots of useful information. In

compression generally the high frequency

component is lost. This high frequency

component in medical imaging paly vital role and

carry some useful information therefore medical

images need to be handled carefully when

applying compression algorithm.

This paper is an attempt to analyze the different

wavelets family based compression on medical

imaging and its effect. This work will definitely

helps us to decide which wavelet family is best

suitable for which type of medical imaging [9].

II. RELATED WORK

Compression is a very vast topic and lots of work

has been done in the past for designing a good

compression algorithm this section briefly

describe some of the noteworthy contribution in

this field.

Wavelet and ridgelet can be used for image

compression and the algorithm based on this is

proposed in the literature[10]. Principal

component analysis and wavelet transform in the

form of combination can also be used in tha past

for image compression[11].Sumithra proposed a

multiwavelet transform based image compression

in his paper which require less computational

time[12].

Meenakshi Choudhary suggested a fast haar

wavelet tarsnform based image compression

which is a combination of wavelet transform and

Singular value decomposition(SVD)[13].

Combination of DCT and DWT can also be used

for image compression [14]

Wavelet based image compression method by

using luminance and chrominance component of

the image is also one of the noteworthy

contribution in this field[15]. Combining the

JPEG techniques with the symbol reduction

algorithm can also be used for image

compression[16]. One of the noteworthy

contribution in image compression is also carried

out in a [17] which is based on the combination

of DCT (Discrete Cosine Transform)and

DWT(Discrete Wavelet Transform). Instead of

using the combination of DCT and DWT in RGB


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space if one use Y,Cb and Cr space then the

combination of the DCT and DWT gives better

result[18]. Some other image compression

techniques which are based on DCT and DWT is

found in [19-25].

III. METHODOLOGY

This work is an attempt to explore the various

wavelet function for compressing the image of

different kind and its effect on the compression

ratio and quality of the image. The Overall block

diagram of the proposed method is shown in the

figure 1. As per the figure 1, first of all a

biomedical image has been taken as a input to

this algorithm. The second block convert the

image in to a gray scale image. This step ensure

that the the image is always a gray scale image to

this system. In the next block image is resize in to

a fixed size. Here we have converted an image in

to size of 256x256 dimension. This is very

important step because later on we have to

comapre all the images. Once all the images are

resized to predefined fixed size then on each

image, wavelet transform is applied using

different wavelet function. The output of this

block is compressed image.

Images are compressed using one level

decomposition and two level decomposition.

This compressed image is then compared with

the original image in term of PSNR(Peak signal

to noise ratio) for quality measure and

compression ratio for finding the amount of

compression achieved.

Figure 1 Block Diagram of Methodology

A. Algorithm Steps for wavelet function based Compression

Step1 Input the image.

Step2 Convert inage in to a gray scale image.

Step3 Resize the image.

Step4 Apply single level/multi level

decomposition using any one wavelet and

obtain all the four frequency band.

Step5 Obtain the book keeping matrix of

coefficient.

Step6 Compute the threshold value for denoising.


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Step7 Compute the number of significant

coefficient required for reconstruction of the

image faithfully.

Step8 using step 6 and step 7 reconstruct the

image.

Step9 Compute the Peak signal to noise

ratio(PSNR) between original and reconstructed

image.

Step10 Compute the Compression ratio between

original and reconstructed image.

IV. EXPERIMENTAL RESULT

Since the main objective of this project is to decide

which wavelet function is best suitable for a given

bio-medical image, therefore 4 different types of

biomedical images have been taken for the analysis

i. X-ray Image

ii. MRI image

iii. Ultrasound Image

iv. Mammography image

In order to see the compression efficiency of

different wavelet function, first of all we set the PSNR

Figure 2 Flow diagram of image compression

using HAAR wavelet function

value for a particular class of bio0medical image.

After analyzing the compression ratio achieved by

the wavelet function, wavelet function which gives

the highest compression ratio is selected as the best

suitable wavelet function for that kind of medical

image for achieving best compression.

The compression process is carried out in two phase.

In the first phase, first and second level of image

decomposition is performed. In the second phase,

reconstruction of the compressed image is achieved

and compute the threshold for the compression and

the PSNR. The value of the threshold and the PSNR is

set to constant for a particular type of image.


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The PSNR value is kept to 5.9866.

Table 1 Compression ratio for X-ray images

Image Type

Wavelet Function

Compression Ratio

PSNR (Fixed)

MRI Images

Haar 3.1591. 5.9866.

Daubechies 3.0691 5.9866.

Coiflets 2.7399 5.9866.

Biorthogonal 2.6914. 5.9866.

Figure 3 Graph between various wavelet functions and their compression ratios for x-ray images

Figure 4 contain the second level decomposition

of x-ray images and reconstructed images using

Haar wavelet. Similarly figure 5,6,8 contain the

second level decomposition of the MRI,

ultrasound and Mammogram images and their

reconstructed part.

Here the table 1 contain the compression ratio of

x-ray images for different types of wavelet

families for fixed PSNR. From this table it is

clear that Haar wavelet is best suitable for

compressing the X-ray images.

Table 2 depicts the compression ration for

different tyupes of wavelet for MRI images and

from this table it is clear that for MRI images

best compression ratio is obtained by again Haar

wavelet.

Table 3 present the compression ration for

ultrasound images and from this table it is clear

that Daubechies wavelet is best for this type of

image compression.

Table 4 represent the compression ration for

mammogram images and from this table it is

evident that Coiflets wavelet is best for

compressing the mammogram images.

Figure 4 Second level decomposition of x-ray images using HAAR WT (Upper) and Origianl and second level

reconstructed image (lower) using HAAR.


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Figure 5 Second level decomposition of MRI images using Daubechies WT (left) and Origianl and second level reconstructed image (Right) using Daubechies WT.

Table 2 Compression ratio for MRI images

Image Type

Wavelet Function

Compression Ratio

PSNR (Fixed)

MRI Images

Haar 3.5227 5.9866.


Coiflets 1.9607 5.9866.

Biorthogonal 1.9608 5.9866.

Figure 6 Second level decomposition of ultrasound images using Coiflets (left) and Origianl and second level reconstructed image (Right) using Coiflets s WT.

Figure 7 Graph between various wavelet functions and their compression ratios for MRI images


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Figure 8 Second level decomposition of mammogram images using Biorthogonal (left) and Origianl and second level reconstructed image (Right) using Biorthogonal WT.

Table 3 Compression ratio for Ultrasound images

Image Type Wavelet Function

Compression Ratio

PSNR (Fixed)

Ultrasound Images

Haar 3.0063 5.9866.


Coiflets 2.8208 5.9866.


Figure 9 Graph between various wavelet functions and their compression ratios for ultrasound images

Table 4 Compression ratio for Mammogram images

Image Type Wavelet Function

Compression Ratio

PSNR (Fixed)

Mammogram Haar 3.0804 5.9866.


Coiflets 3.3726 5.9866.


Figure 10 Graph between various wavelet functions and their compression ratios for Mammography

images


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Peak signal to noise ratio is given by the

following formula

Here

= is the row or column dimension of original

image.

Compression ratio is the measure which is used

to find out how much compression is achieved

using the compression algorithm. It is basically a

ratio of size of original and compressed image

and is given by-

V. CONCLUSION

After exhaustive experiments on different bio-medical images, it can be concluded that all the wavelet function are able to achieve some compression. For X-ray images, wavelet function ‘Haar’ gives the best compression ratio of 3.1591 which is better than the compression ratio of other wavelet function.

As far as MRI images are concerned, it can be comcluded that in MRI images again the wavelet function ‘Haar’ gives the best compression ratio of 3.5227. Compression ration for rest of thye wavelet function is less than this value. So in case of MRI images it will be better to compress the MRI images with Haar wavelet function.

In case of Ultrasound images , the best compression ratio is achieved by using

‘Daubechies’ wavelet function and compression ratio is found to be 3.4008. So it is better to compress the Ultrasound images using ‘Daubechies’ wavelet function.

For Mammogram images, compression ration achieved by the ‘Coiflets’ function is highest as compared to the compression ratio of other wavelet function. So it can be concluded that Mammogram images can be compressed to the maximum level ny wavelet function ‘Coiflets’.

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performance comparison of wavelet family based … · performance comparison of wavelet family...

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